While NeRF has shown great success for neural reconstruction and rendering,
its limited MLP capacity and long per-scene optimization times make it
challenging to model large-scale indoor scenes. In contrast, classical 3D
reconstruction methods can handle large-scale scenes but do not produce
realistic renderings. We propose NeRFusion, a method that combines the
advantages of NeRF and TSDF-based fusion techniques to achieve efficient
large-scale reconstruction and photo-realistic rendering. We process the input
image sequence to predict per-frame local radiance fields via direct network
inference. These are then fused using a novel recurrent neural network that
incrementally reconstructs a global, sparse scene representation in real-time
at 22 fps. This global volume can be further fine-tuned to boost rendering
quality. We demonstrate that NeRFusion achieves state-of-the-art quality on
both large-scale indoor and small-scale object scenes, with substantially
faster reconstruction than NeRF and other recent methods.

NeRFusion 提出了一种方法，通过将 NeRF 和 TSDF-based fusion 技术结合起来，实现了大规模室内场景的高效重建和逼真渲染，使用全新的循环神经网络来递增地重建实时稀疏场景表示，进一步提高了渲染质量，取得了大规模室内和小型物体场景的最先进质量，并比 NeRF 和其他最新方法重建速度更快。